Multiparental Populations Research Articles

Multiparental Population in Crops: Methods of Development and Dissection of Genetic Traits.

Multiparental populations are located midway between association mapping that relies on germplasm collections and classic linkage analysis, based upon biparental populations. They provide several key advantages such as the possibility to include a higher number of alleles and increased level of recombination with respect to biparental populations, and more equilibrated allelic frequencies than association mapping panels. Moreover, in these populations new allele's combinations arise from recombination that may reveal transgressive phenotypes and make them a useful pre-breeding material. Here we describe the strategies for working with multiparental populations, focusing on nested association mapping populations (NAM) and multiparent advanced generation intercross populations (MAGIC). We provide details from the selection of founders, population development, and characterization to the statistical methods for genetic mapping and quantitative trait detection.

Inferring the Allelic Series at QTL in Multiparental Populations.

Multiparental populations (MPPs) are experimental populations in which the genome of every individual is a mosaic of known founder haplotypes. These populations are useful for detecting quantitative trait loci (QTL) because tests of association can leverage inferred founder haplotype descent. It is difficult, however, to determine how haplotypes at a locus group into distinct functional alleles, termed the allelic series. The allelic series is important because it provides information about the number of causal variants at a QTL and their combined effects. In this study, we introduce a fully Bayesian model selection framework for inferring the allelic series. This framework accounts for sources of uncertainty found in typical MPPs, including the number and composition of functional alleles. Our prior distribution for the allelic series is based on the Chinese restaurant process, a relative of the Dirichlet process, and we leverage its connection to the coalescent to introduce additional prior information about haplotype relatedness via a phylogenetic tree. We evaluate our approach via simulation and apply it to QTL from two MPPs: the Collaborative Cross (CC) and the Drosophila Synthetic Population Resource (DSPR). We find that, although posterior inference of the exact allelic series is often uncertain, we are able to distinguish biallelic QTL from more complex multiallelic cases. Additionally, our allele-based approach improves haplotype effect estimation when the true number of functional alleles is small. Our method, Tree-Based Inference of Multiallelism via Bayesian Regression (TIMBR), provides new insight into the genetic architecture of QTL in MPPs.

MAGIC - Multi-parent Advanced Generation Inter-Cross

The development of Multi-parent Advanced Generation Inter-Cross populations (MAGIC) in rice and discusses potential applications for mapping quantitative trait loci (QTLs) and for rice varietal development. The 4 multi-parent populations: indica MAGIC (8 indica parents); MAGIC plus (8 indica parents with two additional rounds of 8-way F1 inter-crossing); japonica MAGIC (8 japonica parents); and Global MAGIC (16 parents – 8 indica and 8 japonica). The parents used in creating these populations are improved varieties with desirable traits for biotic and abiotic stress tolerance, yield, and grain quality. The purpose is to fine map QTLs for multiple traits and to directly and indirectly use the highly recombined lines in breeding programs. These MAGIC populations provide a useful germplasm resource with diverse allelic combinations to be exploited by the rice community.

Association mapping and genomic selection for sorghum adaptation to tropical soils of Brazil in a sorghum multiparental random mating population.

A multiparental random mating population used in sorghum breeding is amenable for the detection of QTLs related to tropical soil adaptation, fine mapping of underlying genes and genomic selection approaches. Tropical soils where low phosphorus (P) and aluminum (Al) toxicity limit sorghum [Sorghum bicolor (L.) Moench] production are widespread in the developing world. We report on BRP13R, a multiparental random mating population (MP-RMP), which is commonly used in sorghum recurrent selection targeting tropical soil adaptation. Recombination dissipated much of BRP13R's likely original population structure and average linkage disequilibrium (LD) persisted up to 2.5Mb, establishing BRP13R as a middle ground between biparental populations and sorghum association panels. Genome-wide association mapping (GWAS) identified conserved QTL from previous studies, such as for root morphology and grain yield under low-P, and indicated the importance of dominance in the genetic architecture of grain yield. By overlapping consensus QTL regions, we mapped two candidate P efficiency genes to a ~ 5Mb region on chromosomes 6 (ALMT) and 9 (PHO2). Remarkably, we find that only 200 progeny genotyped with ~ 45,000 markers in BRP13R can lead to GWAS-based positional cloning of naturally rare, subpopulation-specific alleles, such as for SbMATE-conditioned Al tolerance. Genomic selection was found to be useful in such MP-RMP, particularly if markers in LD with major genes are fitted as fixed effects into GBLUP models accommodating dominance. Shifts in allele frequencies in progeny contrasting for grain yield indicated that intermediate to minor-effect genes on P efficiency, such as SbPSTOL1 genes, can be employed in pre-breeding via allele mining in the base population. Therefore, MP-RMPs such as BRP13R emerge as multipurpose resources for efficient gene discovery and deployment for breeding sorghum cultivars adapted to tropical soils.

SPEARS: Standard Performance Evaluation of Ancestral haplotype Reconstruction through Simulation.

MotivationAncestral haplotype maps provide useful information about genomic variation and insights into biological processes. Reconstructing the descendent haplotype structure of homologous chromosomes, particularly for large numbers of individuals, can help with characterizing the recombination landscape, elucidating genotype-to-phenotype relationships, improving genomic predictions and more. Inferring haplotype maps from sparse genotype data is an efficient approach to whole-genome haplotyping, but this is a non-trivial problem. A standardized approach is needed to validate whether haplotype reconstruction software, conceived population designs and existing data for a given population provides accurate haplotype information for further inference.ResultsWe introduce SPEARS, a pipeline for the simulation-based appraisal of genome-wide haplotype maps constructed from sparse genotype data. Using a specified pedigree, the pipeline generates virtual genotypes (known data) with genotyping errors and missing data structure. It then proceeds to mimic analysis in practice, capturing sources of error due to genotyping, imputation and haplotype inference. Standard metrics allow researchers to assess different population designs and which features of haplotype structure or regions of the genome are sufficiently accurate for analysis. Haplotype maps for 1000 outcross progeny from a multi-parent population of maize are used to demonstrate SPEARS.Availabilityand implementationSPEARS, the protocol and suite of scripts, are publicly available under an MIT license at GitHub (https://github.com/maizeatlas/spears).Supplementary information Supplementary data are available at Bioinformatics online.

Bin-based genome-wide association studies reveal superior alleles for improvement of appearance quality using a 4-way MAGIC population in rice

IntroductionThe multiparental population provides us the chance to identify superior alleles controlling a trait for genetic improvement. Genome wide association studies at bin level (bin-GWAS) are expected to be more power in QTL mapping than GWAS at SNP level (SNP-GWAS). ObjectivesThis study is to estimate genetic effects of QTL conferring grain appearance quality in rice by SNP-GWAS and bin-GWAS, compare their power in QTL mapping and identify the superior alleles of all detected QTL from 4 parents for genetic improvement. MethodsA 4-way MAGIC population and its four founders were cultivated in two environments to dissect the genetic basis of rice grain appearance quality. Both SNP-GWAS and bin-GWAS were conducted for QTL mapping. Multiple comparison among 4 parental bin/alleles was used to identify the superior alleles. ResultsA total of 16 and 20 QTL associated with grain appearance quality were identified by SNP- and bin-GWAS, respectively. A minor chalkiness QTL qPGWC8.2/qDEC8 was assigned to a 30-kb genomic region, in which OsMH_08T0121900 is the potential candidate gene because its encoded protein, glucan endo-1,3-beta-glucosidase precursor is involved in the starch and sucrose metabolism pathway. The superior parental alleles for GS3, GL3.1, GW5, GW7, and Chalk5 and two QTLs were almost carried by the high-quality parents Cypress and Yuejingsimiao (YJSM), while the poor-quality parent Guichao-2 (GC2) always carried the inferior alleles. The top five recombinant inbred lines with the highest quality of grain shape and chalkiness traits all carried gene combinations of superior alleles. ConclusionsBoth SNP- and bin-GWAS methods are encouraged for joint QTL mapping with MAGIC population. qPGWC8.2/qDEC8 is a novel candidate gene strongly associated with chalkiness. The superior alleles of GS3, GW5, GL3.1, GW7, Chalk5 and qPGWC8.2 were identified, and the pyramiding of these superior alleles is helpful to improve rice appearance quality.

Integration of QTL, Transcriptome and Polymorphism Studies Reveals Candidate Genes for Water Stress Response in Tomato.

Water deficit (WD) leads to significant phenotypic changes in crops resulting from complex stress regulation mechanisms involving responses at the physiological, biochemical and molecular levels. Tomato growth and fruit quality have been shown to be significantly affected by WD stress. Understanding the molecular mechanism underlying response to WD is crucial to develop tomato cultivars with relatively high performance under low watering conditions. Transcriptome response to WD was investigated through the RNA sequencing of fruit and leaves in eight accessions grown under two irrigation conditions, in order to get insight into the complex genetic regulation of WD response in tomato. Significant differences in genotype WD response were first observed at the phenotypic level for fruit composition and plant development traits. At the transcriptome level, a total of 14,065 differentially expressed genes (DEGs) in response to WD were detected, among which 7393 (53%) and 11,059 (79%) were genotype- and organ-specific, respectively. Water deficit induced transcriptome variations much stronger in leaves than in fruit. A significant effect of the genetic background on expression variation was observed compared to the WD effect, along with the presence of a set of genes showing a significant genotype × watering regime interaction. Integrating the DEGs with previously identified WD response quantitative trait loci (QTLs) mapped in a multi-parental population derived from the crossing of the eight genotypes narrowed the candidate gene lists to within the confidence intervals surrounding the QTLs. The results present valuable resources for further study to decipher the genetic determinants of tomato response to WD.

Use of family structure information in interaction with environments for leveraging genomic prediction models

The characterization of genomes with great detail offered by the modern genotyping platforms have opened a venue for accurately predicting the genotype-by-environment interaction (GE) effects of untested genotypes in different environmental conditions. Already developed statistical models have shown the advantages of including the GE interaction component in the prediction context using molecular markers, pedigree, or both. In order to leverage the family information of highly structured populations when pedigree data is not available, we developed a model that uses the family membership instead. The proposed model extends the reaction norm model by including the interaction between families and environments (FE). A representative fraction of a soybean Nested Association Mapping population (16,187 grain yield records) comprising 38 bi-parental families (1358 genotypes) observed in 18 environments (2011, 2012, and 2013) was used to contrast the proposed model with three conventional prediction models. Two cross-validation scenarios (prediction of tested [CV2] and untested [CV1] genotypes) with a twofold design (50% for training and testing sets) were used for mimicking prediction situations that breeders face in fields. Results showed that the family factor in interaction with environments explains a sizable amount of the phenotypic variability. This helped to improve the predictive ability with respect to the main effects model (GBLUP) around 41% (CV2) and 49% (CV1), and about 17% with respect to the conventional reaction norm model. The inclusion of the FE term not only improved the global results but also significantly increased the prediction accuracy of those environments where the conventional models showed a very poor performance. These results show the importance of taking into consideration the family structure existing in breeding programs for improving the selection strategies in multi-parental populations.

Origin Specific Genomic Selection: A Simple Process To Optimize the Favorable Contribution of Parents to Progeny.

Modern crop breeding is in constant demand for new genetic diversity as part of the arms race with genetic gain. The elite gene pool has limited genetic variation and breeders are trying to introduce novelty from unadapted germplasm, landraces and wild relatives. For polygenic traits, currently available approaches to introgression are not ideal, as there is a demonstrable bias against exotic alleles during selection. Here, we propose a partitioned form of genomic selection, called Origin Specific Genomic Selection (OSGS), where we identify and target selection on favorable exotic alleles. Briefly, within a population derived from a bi-parental cross, we isolate alleles originating from the elite and exotic parents, which then allows us to separate out the predicted marker effects based on the allele origins. We validated the usefulness of OSGS using two nested association mapping (NAM) datasets: barley NAM (elite-exotic) and maize NAM (elite-elite), as well as by computer simulation. Our results suggest that OSGS works well in its goal to increase the contribution of favorable exotic alleles in bi-parental crosses, and it is possible to extend the approach to broader multi-parental populations.

Multi-parent multi-environment QTL analysis: an illustration with the EU-NAM Flint population

Key messageMulti-parent populations multi-environment QTL experiments data should be analysed jointly to estimate the QTL effect variation within the population and between environments.Commonly, QTL detection in multi-parent populations (MPPs) data measured in multiple environments (ME) is done by analyzing genotypic values ‘averaged’ across environments. This method ignores the environment-specific QTL (QTLxE) effects. Running separate single environment analyses is a possibility to measure QTLxE effects, but those analyses do not model the genetic covariance due to the use of the same genotype in different environments. In this paper, we propose methods to analyse MPP-ME QTL experiments using simultaneously the data from several environments and modelling the genotypic covariance. Using data from the EU-NAM Flint population, we show that these methods estimate the QTLxE effects and that they can improve the quality of the QTL detection. Those methods also have a larger inference power. For example, they can be extended to integrate environmental indices like temperature or precipitation to better understand the mechanisms behind the QTLxE effects. Therefore, our methodology allows the exploitation of the full MPP-ME data potential: to estimate QTL effect variation (a) within the MPP between sub-populations due to different genetic backgrounds and (b) between environments.

Two Synthetic 18-Way Outcrossed Populations of Diploid Budding Yeast with Utility for Complex Trait Dissection.

Advanced-generation multiparent populations (MPPs) are a valuable tool for dissecting complex traits, having more power than genome-wide association studies to detect rare variants and higher resolution than F2 linkage mapping. To extend the advantages of MPPs in budding yeast, we describe the creation and characterization of two outbred MPPs derived from 18 genetically diverse founding strains. We carried out de novo assemblies of the genomes of the 18 founder strains, such that virtually all variation segregating between these strains is known, and represented those assemblies as Santa Cruz Genome Browser tracks. We discovered complex patterns of structural variation segregating among the founders, including a large deletion within the vacuolar ATPase VMA1, several different deletions within the osmosensor MSB2, a series of deletions and insertions at PRM7 and the adjacent BSC1, as well as copy number variation at the dehydrogenase ALD2 Resequenced haploid recombinant clones from the two MPPs have a median unrecombined block size of 66 kb, demonstrating that the population is highly recombined. We pool-sequenced the two MPPs to 3270× and 2226× coverage and demonstrated that we can accurately estimate local haplotype frequencies using pooled data. We further downsampled the pool-sequenced data to ∼20-40× and showed that local haplotype frequency estimates remained accurate, with median error rates 0.8 and 0.6% at 20× and 40×, respectively. Haplotypes frequencies are estimated much more accurately than SNP frequencies obtained directly from the same data. Deep sequencing of the two populations revealed that 10 or more founders are present at a detectable frequency for > 98% of the genome, validating the utility of this resource for the exploration of the role of standing variation in the architecture of complex traits.

Multiparental QTL analysis: can we do it in polyploids?

Many ornamental crops are polyploid or even exist at different ploidy levels. Polyploid QTL analysis tools have been developed in recent years, yet they are limited in the population types they accept. Biparental populations are nowadays being regarded as a limited tool for QTL discovery, as only a limited number of QTLs occurs in an experimental cross and their effects might not be stable across genetic backgrounds. Genome-Wide Association Studies include more genetic diversity but suffer from (hidden) genetic structure and low frequency of QTL alleles. Both factors influence QTL detection and effect estimation, decreasing the sensitivity of QTL analysis. Alternatively, multiparental populations (MPP) can be used, potentially combining multiple QTLs and QTL alleles with known population structure and balanced allele frequencies. Breeding populations of interconnected crosses also constitute a form of MPP and QTLs identified in them might be more applicable to commercial cultivars. To perform QTL analysis in polyploids, mixed models or Bayesian approaches that consider pedigree information are recommended. During the analysis, QTL effects are ideally estimated using Identity by Descent (IBD) alleles (genomic regions that originate from the same ancestor) which can be obtained through haplotype estimation. Although MPPs could thus be a powerful set-up to estimate polyploid haplotypes, a software gap was identified as no current polyploid haplotyping tools are able to utilize MPP pedigree information to obtain haplotypes across an MPP. In order to utilize MPPs to their full extent and expand polyploid QTL analyses to encompass typical breeding populations, new haplotyping tools must be developed.

Genetic basis of phenotypic plasticity and genotype × environment interactions in a multi-parental tomato population.

Deciphering the genetic basis of phenotypic plasticity and genotype × environment interactions (G×E) is of primary importance for plant breeding in the context of global climate change. Tomato (Solanum lycopersicum) is a widely cultivated crop that can grow in different geographical habitats and that displays a great capacity for expressing phenotypic plasticity. We used a multi-parental advanced generation intercross (MAGIC) tomato population to explore G×E and plasticity for multiple traits measured in a multi-environment trial (MET) comprising optimal cultural conditions together with water deficit, salinity, and heat stress over 12 environments. Substantial G×E was observed for all the traits measured. Different plasticity parameters were estimated by employing Finlay-Wilkinson and factorial regression models and these were used together with genotypic means for quantitative trait loci (QTL) mapping analyses. In addition, mixed linear models were also used to investigate the presence of QTL × environment interactions. The results highlighted a complex genetic architecture of tomato plasticity and G×E. Candidate genes that might be involved in the occurrence of G×E are proposed, paving the way for functional characterization of stress response genes in tomato and for breeding climate-adapted cultivars.

Accurate calling of homeoallelic genotypes of iSelect markers using inbred structured populations

Genetic map construction is a foundational step in the analysis of structured experimental populations. For markers that hybridize to several genetically similar locations, or where several alleles are present (such as in multiparental populations), current methods often discard the marker or incorrectly call the genotypes. These errors result in information loss, or incorrect genotypes that can corrupt map construction. We present a new approach for simultaneously performing genetic map construction and marker calling. Our new approach allows the calling of a larger number of markers, a larger number of unique alleles per marker and the correct use of markers which hybridize to multiple genetically similar locations. We demonstrate our new approach using simulations, a biparental wheat population and an eight-parent population of spring bread wheat. Applying our method to the eight-parent population increased the number of mapped markers by 71%. We show that the new genetic map allows the investigation of synteny in ways that were not previously possible in that dataset. The method described in this article has been incorporated into R package mpMap2. It is available from CRAN and also from https://github.com/rohan-shah/mpMap2. Supplementary data are available at Bioinformatics online.

Revisiting hybrid breeding designs using genomic predictions: simulations highlight the superiority of incomplete factorials between segregating families over topcross designs.

Simulations showed that hybrid performances issued from an incomplete factorial between segregating families of two heterotic groups enable to calibrate genomic predictions of hybrid value more efficiently than tester-based designs. Genomic selection offers new opportunities to revisit hybrid breeding by replacing extensive phenotyping of hybrid combinations by genomic predictions. A key question remains to identify the best design to calibrate genomic prediction models. We proposed to use single-cross hybrids issued from an incomplete factorial design between segregating populations and compared this strategy with a conventional approach based on topcross evaluation. Two multiparental segregating populations of lines, each specific of one heterotic group, were simulated. Hybrids considered as training sets were generated using either (1) a parental line from the opposite group as tester or (2) following an incomplete factorial design. Different specific combining ability (SCA) proportions were simulated by considering different levels of group divergence and dominance effects for the simulated QTL. For the incomplete factorial design, for a same number of hybrids, we considered different numbers of parental lines and different contributions of lines (one to four) to calibration hybrids. We evaluated for different training set sizes prediction accuracies of new hybrids and genetic gains along three generations. At a given training set size, factorial design was as efficient (considering accuracy) as tester design in additive scenarios, but significantly outperformed tester design when SCA was present. The contribution number of each parental line to the incomplete factorial design had a small impact on accuracies. Our simulations confirmed experimental results and showed that calibrating models on hybrids between two multiparental populations is a cost-efficient way to perform genomic predictions in both groups, opening prospects for revisiting reciprocal recurrent selection schemes.

Gene presence-absence variation associates with quantitative Verticillium longisporum disease resistance in Brassica napus

Although copy number variation (CNV) and presence-absence variation (PAV) have been discovered in selected gene families in most crop species, the global prevalence of these polymorphisms in most complex genomes is still unclear and their influence on quantitatively inherited agronomic traits is still largely unknown. Here we analyze the association of gene PAV with resistance of oilseed rape (Brassica napus) against the important fungal pathogen Verticillium longisporum, as an example for a complex, quantitative disease resistance in the strongly rearranged genome of a recent allopolyploid crop species. Using Single Nucleotide absence Polymorphism (SNaP) markers to efficiently trace PAV in breeding populations, we significantly increased the resolution of loci influencing V. longisporum resistance in biparental and multi-parental mapping populations. Gene PAV, assayed by resequencing mapping parents, was observed in 23–51% of the genes within confidence intervals of quantitative trait loci (QTL) for V. longisporum resistance, and high-priority candidate genes identified within QTL were all affected by PAV. The results demonstrate the prominent role of gene PAV in determining agronomic traits, suggesting that this important class of polymorphism should be exploited more systematically in future plant breeding.

Construction and integration of genetic linkage maps from three multi-parent advanced generation inter-cross populations in rice

BackgroundThe construction of genetic maps based on molecular markers is a crucial step in rice genetic and genomic studies. Pure lines derived from multiple parents provide more abundant genetic variation than those from bi-parent populations. Two four-parent pure-line populations (4PL1 and 4PL2) and one eight-parent pure-line population (8PL) were developed from eight homozygous indica varieties of rice by the International Rice Research Institute (IRRI). To the best of our knowledge, there have been no reports on linkage map construction and their integration in multi-parent populations of rice.ResultsWe constructed linkage maps for the three multi-parent populations and conducted quantitative trait locus (QTL) mapping for heading date (HD) and plant height (PH) based on the three maps by inclusive composite interval mapping (ICIM). An integrated map was built from the three individual maps and used for QTL projection and meta-analysis. QTL mapping of the three populations was also conducted based on the integrated map, and the mapping results were compared with those from meta-analysis. The three linkage maps developed for 8PL, 4PL1 and 4PL2 had 5905, 4354 and 5464 bins and were 1290.16, 1720.01 and 1560.30 cM in length, respectively. The integrated map was 3022.08 cM in length and contained 10,033 bins. Based on the three linkage maps, 3, 7 and 9 QTLs were detected for HD while 6, 9 and 10 QTLs were detected for PH in 8PL, 4PL1 and 4PL2, respectively. In contrast, 19 and 25 QTLs were identified for HD and PH by meta-analysis using the integrated map, respectively. Based on the integrated map, 5, 9, and 10 QTLs were detected for HD while 3, 10, and 12 QTLs were detected for PH in 8PL, 4PL1 and 4PL2, respectively. Eleven of these 49 QTLs coincided with those from the meta-analysis.ConclusionsIn this study, we reported the first rice linkage map constructed from one eight-parent recombinant inbred line (RIL) population and the first integrated map from three multi-parent populations, which provide essential information for QTL linkage mapping, meta-analysis, and map-based cloning in rice genetics and breeding.

Diverse biological processes coordinate the transcriptional response to nutritional changes in a Drosophila melanogaster multiparent population

BackgroundEnvironmental variation in the amount of resources available to populations challenge individuals to optimize the allocation of those resources to key fitness functions. This coordination of resource allocation relative to resource availability is commonly attributed to key nutrient sensing gene pathways in laboratory model organisms, chiefly the insulin/TOR signaling pathway. However, the genetic basis of diet-induced variation in gene expression is less clear.ResultsTo describe the natural genetic variation underlying nutrient-dependent differences, we used an outbred panel derived from a multiparental population, the Drosophila Synthetic Population Resource. We analyzed RNA sequence data from multiple female tissue samples dissected from flies reared in three nutritional conditions: high sugar (HS), dietary restriction (DR), and control (C) diets. A large proportion of genes in the experiment (19.6% or 2471 genes) were significantly differentially expressed for the effect of diet, and 7.8% (978 genes) for the effect of the interaction between diet and tissue type (LRT, Padj. < 0.05). Interestingly, we observed similar patterns of gene expression relative to the C diet, in the DR and HS treated flies, a response likely reflecting diet component ratios. Hierarchical clustering identified 21 robust gene modules showing intra-modularly similar patterns of expression across diets, all of which were highly significant for diet or diet-tissue interaction effects (FDR Padj. < 0.05). Gene set enrichment analysis for different diet-tissue combinations revealed a diverse set of pathways and gene ontology (GO) terms (two-sample t-test, FDR < 0.05). GO analysis on individual co-expressed modules likewise showed a large number of terms encompassing many cellular and nuclear processes (Fisher exact test, Padj. < 0.01). Although a handful of genes in the IIS/TOR pathway including Ilp5, Rheb, and Sirt2 showed significant elevation in expression, many key genes such as InR, chico, most insulin peptide genes, and the nutrient-sensing pathways were not observed.ConclusionsOur results suggest that a more diverse network of pathways and gene networks mediate the diet response in our population. These results have important implications for future studies focusing on diet responses in natural populations.

Nested-association mapping (NAM)-based genetic dissection uncovers candidate genes for seed and pod weights in peanut (Arachis hypogaea).

SummaryMultiparental genetic mapping populations such as nested‐association mapping (NAM) have great potential for investigating quantitative traits and associated genomic regions leading to rapid discovery of candidate genes and markers. To demonstrate the utility and power of this approach, two NAM populations, NAM_Tifrunner and NAM_Florida‐07, were used for dissecting genetic control of 100‐pod weight (PW) and 100‐seed weight (SW) in peanut. Two high‐density SNP‐based genetic maps were constructed with 3341 loci and 2668 loci for NAM_Tifrunner and NAM_Florida‐07, respectively. The quantitative trait locus (QTL) analysis identified 12 and 8 major effect QTLs for PW and SW, respectively, in NAM_Tifrunner, and 13 and 11 major effect QTLs for PW and SW, respectively, in NAM_Florida‐07. Most of the QTLs associated with PW and SW were mapped on the chromosomes A05, A06, B05 and B06. A genomewide association study (GWAS) analysis identified 19 and 28 highly significant SNP–trait associations (STAs) in NAM_Tifrunner and 11 and 17 STAs in NAM_Florida‐07 for PW and SW, respectively. These significant STAs were co‐localized, suggesting that PW and SW are co‐regulated by several candidate genes identified on chromosomes A05, A06, B05, and B06. This study demonstrates the utility of NAM population for genetic dissection of complex traits and performing high‐resolution trait mapping in peanut.

Components of the ribosome biogenesis pathway underlie establishment of telomere length set point in Arabidopsis

Telomeres cap the physical ends of eukaryotic chromosomes to ensure complete DNA replication and genome stability. Heritable natural variation in telomere length exists in yeast, mice, plants and humans at birth; however, major effect loci underlying such polymorphism remain elusive. Here, we employ quantitative trait locus (QTL) mapping and transgenic manipulations to identify genes controlling telomere length set point in a multi-parent Arabidopsis thaliana mapping population. We detect several QTL explaining 63.7% of the total telomere length variation in the Arabidopsis MAGIC population. Loss-of-function mutants of the NOP2A candidate gene located inside the largest effect QTL and of two other ribosomal genes RPL5A and RPL5B establish a shorter telomere length set point than wild type. These findings indicate that evolutionarily conserved components of ribosome biogenesis and cell proliferation pathways promote telomere elongation.